code: def apply(
      cls, ctx: Context, inp: Inputs, grad: torch.Tensor
  ) -> Gradients:
      if type(inp.attn_bias) not in BwOp.SUPPORTED_ATTN_BIAS_TYPES:
          raise NotImplementedError("Unsupported attn_bias type")
  
      (
          seqstart_k,
          seqstart_q,
          max_seqlen_q,
          max_seqlen_k,
      ) = _get_seqlen_info(inp)
      dtype = inp.query.dtype
  
      rng_seed = rng_offset = 0
      if inp.p != 0.0:
          if (
              ctx.rng_state is None
              or ctx.rng_state.dtype != torch.int64
              or ctx.rng_state.device.type != "cpu"
              or ctx.rng_state.shape != (2,)
          ):
              raise NotImplementedError(
                  f"Invalid rng_state: {ctx.rng_state}"
              )
          rng_seed, rng_offset = ctx.rng_state.tolist()
  
      force_pad_inf = torch.cuda.get_device_capability(
          inp.query.device
      ) == (7, 5)
      (grad_q, grad_k, grad_v, grad_bias) = cls.OPERATOR(
          grad.to(dtype),
          inp.query,
          inp.key,
          inp.value,
          _get_tensor_bias(inp.attn_bias),
          cu_seqlens_q=seqstart_q,
          cu_seqlens_k=seqstart_k,
          max_seqlen_q=max_seqlen_q,
          max_seqlen_k=max_seqlen_k,
          logsumexp=ctx.get_padded_lse(32, force_pad_inf=force_pad_inf),
          output=ctx.out.to(dtype),
          dropout_p=inp.p,
          # if not using dropout, seed and offset are irrelevant but still expected
          # in function signature so just pass 0
          # seed and offset could be None if a different FW op other than cutlass
          # was used.
          rng_seed=rng_seed,
          rng_offset=rng_offset,
          custom_mask_type=_custom_mask_type(inp.attn_bias),
          scale=inp.scale,
          num_splits_key=-1,  # Let C++ determine it
          window_size=inp.attn_bias._window_size
          if isinstance(
              inp.attn_bias,
              (
                  BlockDiagonalCausalLocalAttentionMask,
                  BlockDiagonalCausalLocalAttentionFromBottomRightMask,
                  LowerTriangularFromBottomRightLocalAttentionMask,
              ),
          )
          else None,
      )
  
      # c++/CUDA implementation returns an uninitialized tensor if bias doesn't
      # require grad
      if not (
          isinstance(inp.attn_bias, torch.Tensor)
          and inp.attn_bias.requires_grad
      ):
          grad_bias = None
  
      return Gradients(dq=grad_q, dk=grad_k, dv=grad_v, db=grad_bias)
  
imports: from dataclasses import replace
  from enum import Enum
  from functools import partial
  from typing import Any, List, Optional, Set, Tuple, Union
  import torch
  from ..common import get_xformers_operator, register_operator
  from .None import attn_bias
  from .attn_bias import AttentionBias, BlockDiagonalCausalLocalAttentionFromBottomRightMask, BlockDiagonalCausalLocalAttentionMask, BlockDiagonalCausalMask, BlockDiagonalCausalWithOffsetPaddedKeysMask, BlockDiagonalMask, LowerTriangularFromBottomRightLocalAttentionMask, LowerTriangularFromBottomRightMask, LowerTriangularMask, LowerTriangularMaskWithTensorBias
  from .common import AttentionBwOpBase, AttentionFwOpBase, Context, Gradients, Inputs, _attn_bias_apply, check_lastdim_alignment_stride1
label_desc: Use image node_id 2 for calling the BwOp obj's underlying member method code with example usage: obj.apply(cls, ctx, inp, grad) and returns: Gradients